Long Short-Term Memory-based Sentiment Classification of Cloud Dataset
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2021 Innovations in Intelligent Systems and Applications Conference (ASYU), 2021, 00, pp. 1-6
- Issue Date:
- 2021-11-18
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Long_Short-Term_Memory-based_Sentiment_Classification_of_Cloud_Dataset.pdf | Published version | 663.31 kB |
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Text Sentiment Classification is a crucial task for various decision-making processes in many organizations. It identifies the polarity of texts positively and negatively and highlights the opinions and views hidden within the comments or reviews of a product or service. Performing it on big data from social media and related sources is quite tricky and time-consuming. Nowadays, Deep Learning (DL) is widely used for sentiment analysis due to its high performance. In this paper, Recurrent Neural Network (RNN) based Long Short-Term Memory (LSTM) approach is applied to perform sentiment analysis of a cloud review dataset. The cloud review dataset contains cloud consumer reviews regarding the services provided by different cloud service providers and the dataset is achieved as a result of the Harvesting-as-a-Service (HaaS) framework. The study focuses on observing the behaviour of the deep learning RNN-LSTM approach on a cloud dataset. Results of the experiment are evaluated using various evaluation and performance metrics. The approach tends to achieve 95 % accuracy.
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